Background: Egypt is one of the top steel producers in the Middle East and Africa, yet it faces acute water scarcity and rising energy costs, making it a critical context for studying trade-offs among carbon emissions, water ecological effects, and operational cost in steel supply chain. Methods: Using a multi-objective optimization model based on real data from a major Egyptian steel manufacturer, this study evaluates trade-offs among cost, tardiness, and environmental impact measured by carbon emissions and water ecological effects. Unlike prior studies, this study demonstrates that dedicated warehousing enables batch-level traceability of returned scrap while reducing material handling travel time and carbon emissions. The AUGMECON method generates Pareto-optimal solutions, and sensitivity analysis is conducted on six parameters: scrap take-back rate, demand variability, raw material price, energy cost, production capacity, and carbon tax. Results: Demand and raw material prices dominate performance: a 5% demand increase raises cost by 8.6%, and a 15% raw material price increase raises cost by 32.7%. The knee-point solution achieves 58.18 billion EGP, 0.99 months tardiness, and 2096 million kg CO2 over nine months. Conclusions: This study quantifies the impact of the circular economy and operational parameters on steel supply chain performance under a dedicated warehousing policy.

Abstract Additive manufacturing (AM) offers significant advantages over conventional manufacturing, including reduced material waste, high precision, and tailored mechanical properties. However, the rapid proliferation of AM technologies complicates technology selection, especially in emerging contexts such as Egypt, where local economic conditions, material availability, and technical expertise impose constraints. This study develops a decision-support tool for selecting the most suitable AM technology for the Egyptian industry using a fuzzy VIKOR-based multi-criteria decision-making framework. Eleven criteria, identified through a survey of Egyptian professionals, cover application suitability and material/technical compatibility. Three candidate technologies, namely fused deposition modeling (FDM), selective laser sintering (SLS), and stereolithography (SLA), are evaluated using linguistic judgments from three decision-makers converted to triangular fuzzy numbers. FDM ranks first with Q=0, followed by SLS (Q=0.22) and SLA (Q=1.0). FDM’s superiority is attributed to its strong performance on compatibility with plastics/polymers, which is the predominant material class in Egyptian AM usage (67%) as revealed by a conducted survey. A systematic sensitivity analysis (varying each criterion weight by ±10% and ±20%) reveals that FDM remains top-ranked in 88.6% of scenarios. SLS becomes optimal in 11.4% of scenarios, specifically when increased emphasis is placed on design review or laser-based technology compatibility. SLA never achieves top ranking. The proposed framework provides a transparent, context-appropriate tool enabling Egyptian manufacturers to select AM technologies aligned with local capabilities, reducing investment risks and enhancing industrial competitiveness. The methodology is adaptable to other emerging economies facing similar constraints.

This paper examines the impact of two storage policies—dedicated storage (D-SLAP) and randomized storage (R-SLAP)—on warehouse operational efficiency. It integrates the Storage Location Assignment Problem (SLAP) with the unrelated parallel machine scheduling problem (UPMSP), which represents the scheduling of the material handling equipment (MHE). This integration is intended to elucidate the interplay between storage strategies and scheduling performance. The considered evaluation metrics include transportation cost, average waiting time, and total tardiness, while accounting for product arrival and demand schedules, precedence constraints, and transportation expenses. Additionally, considerations such as MHE eligibility, resource requirements, and available storage locations are incorporated into the analysis. Given the complexity of the combined problem, a tailored Non-dominated Sorting Genetic Algorithm (NSGA-II) was developed to assess the performance of the two storage policies across various randomly generated test instances of differing sizes. Parameter tuning for the NSGA-II was conducted using the Taguchi method to identify optimal settings. Experimental and statistical analyses reveal that, for small-size instances, both policies exhibit comparable performance in terms of transportation cost and total tardiness, with R-SLAP demonstrating superior performance in reducing average waiting time. Conversely, results from large-size instances indicate that D-SLAP surpasses R-SLAP in optimizing waiting time and tardiness objectives, while R-SLAP achieves lower transportation cost.

Smart energy management is critical for reducing household electricity costs and addressing rising demand. While appliance scheduling is often explored under dynamic pricing schemes, this study demonstrates its effectiveness under Inclining Block Rate (IBR) tariffs—commonly used in developing countries and currently applied in Egypt. This paper proposes a Mixed-Integer Quadratic Programming (MIQP) model for optimal appliance scheduling, incorporating user-defined daily budget limits, utility preferences, and photovoltaic (PV) integration under net metering. The model is validated using synthetic test cases and empirical data from a household survey, alongside actual solar generation profiles. All analyses reflect Egypt’s residential IBR tariff structure and PV feed-in rates as of 2023. Results show that low-income households (350 kWh/month) with a 2.5 kW PV system achieve full energy self-sufficiency under a 5 EGP/day budget, generating a surplus. Without PV, the same group meets essential needs under a 15 EGP/day budget with a net expense of 11.55 EGP/day. High-income households (1000 kWh/month) reach maximum utility under a 60 EGP/day budget. These findings confirm that appliance load scheduling is viable and impactful under IBR tariffs. Sensitivity analysis results demonstrate that beyond a certain threshold, increasing the daily budget limit yields no further gains in user satisfaction. This highlights the optimal budget levels households should aim for under the IBR tariff. The model offers a scalable, budget-aware framework to support smart energy management and PV adoption across Egyptian households and similar emerging-economy contexts.

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan) and the minimization of the mean weighted flow time. Both are particularly important for improving machines’ utilization and customer satisfaction level in maintenance and healthcare diagnostic systems, in which the studied Bi-DMOSP is mostly encountered. Since the studied problem is NP-hard for both objectives, fast algorithms are needed to fulfill the requirements of real-life circumstances. Previous attempts have included the development of an exact algorithm and two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective gray wolf optimizer (MOGWO). The exact algorithm is limited to small-sized instances; meanwhile, NSGA-II was found to produce better results compared to MOGWO in both small- and large-sized test instances. The proposed MOSS in this paper attempts to provide more efficient non-dominated solutions for the studied Bi-DMOSP. This is achievable via its hybridization with a novel, bi-objective tabu search approach that utilizes a set of efficient neighborhood search functions. Parameter tuning experiments are conducted first using a subset of small-sized benchmark instances for which the optimal Pareto front solutions are known. Then, detailed computational experiments on small- and large-sized instances are conducted. Comparisons with the previously developed NSGA-II metaheuristic demonstrate the superiority of the proposed MOSS approach for small-sized instances. For large-sized instances, it proves its capability of producing competitive results for instances with low and medium density.

As the number of alternative machines has increased and their technology has been continuously developed, the machine selection problem has attracted many researchers. This article reviews recent developments in applying multi-criteria decision-making (MCDM) methods for selecting machines in the manufacturing and construction industries. Selected articles are classified according to the application area and the applied MCDM method. By focusing on the last five years, this paper identifies recent trends in developing and using these methods. Results suggest that there has been a noticeable growth in the utilization of MCDM techniques for machine selection problems in both sectors. It is also noted that several decision-support tools and methods have been developed and successfully applied during this period. Accordingly, needs and directions for future research are discussed.

Hybrid flowshops are a special type of manufacturing systems, in which a stage may contain identical or unrelated parallel machines. This paper deals with a more practical approach for lot streaming hybrid flowshop in which the sublot sizes of jobs can vary from one stage to the next according to machines' speed. Two models of mixed-integer nonlinear programming are developed to minimise the make-span of two different hybrid flowshop systems. The first model deals with unrelated parallel machines with eligibility, independent setup time, and variable sublot sizes. The second model is a special case of the hybrid flowshop as it consists of multi-stages comprising one machine at the stages preceding the final stage, while the final stage includes unrelated parallel machines. The first model was studied and the data gathered were analysed using ANOVA test to evaluate the factors' effect on system. The factors are number of jobs, maximum number of batches, setup time, and machine's configuration. The analysis revealed that all the factors were effective. The second model was compared to benchmarking published paper and it gets better results.

Frost affects horticultural plants considerably and result in multi-dimensional harms: from economic losses to psychological problems for people involved in horticulture. As a result, prevention of frost in horticulture is of utter most importance for many countries. In this paper, first we propose a novel green energy-integrated solution, a hybrid renewable energy-based system involving active heaters, for this less studied, but very important problem. We then develop a multi-objective robust optimization-based formulation for optimization of the proposed system in order to (i) optimize the distribution of a given number of active heaters in a given large-scale orchard to optimally heat the orchard by the proposed frost prevention system and (ii) optimize the layout of the thermal energy distribution network to minimize the total pipe length (which is directly related to the installation cost and the cost of energy losses during energy transfer). Finally, the resulting optimization problem is approximated using a discretization scheme. A case study is provided to give an idea of the potential savings using the proposed optimization method compared to the result from a heuristic-based design, which showed a 24.13% reduction in the total pipe length and a 54.29% increase in optimal heating. Compared to current active frost prevention methods, the proposed hybrid green energy system for frost protection is a cleaner, environmentally friendly and potentially cost-effective solution.

This paper addresses a bi-objective dynamic multiprocessor open shop scheduling problem in which the simultaneous objectives of minimizing both the mean weighted flow time and the makespan are considered. This problem is commonly encountered in maintenance and healthcare diagnostic systems. Since it is NP-hard for both objectives, efficient heuristics are needed to quickly generate a set of non-dominated solutions that a decision maker would choose from. For this sake, two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective grey wolf optimizer (MOGWO) are developed in this paper. Both metaheuristics are hybridized with simulated annealing (SA) local search. Parameter tuning computational experiments are conducted first on a set of 30 small instances from the literature for which Pareto optimal solutions are known. Then, computational experiments on large randomly generated instances are conducted. Computational results for small instances show that the NSGA-II is capable of generating non-dominated solutions that are very close to the optimal Pareto front. Results also reveal that the performance of the NSGA-II is better in most of the cases compared to the MOGWO under different settings of the studied problem for both small and large instances. However, for large instances with large number of workstations and jobs, low loading level and high percentage of busy machines at the beginning of the schedule, the difference in performance between both metaheuristics is minor.

Maintenance and health care diagnostic systems are generally composed of different workstations pertaining to technologically different processes. A workstation is composed of one or more parallel machines. In such systems, the multiprocessor open shop scheduling problem is commonly encountered. It is concerned with assigning processing intervals for each job on machines that need to be selected in each requested workstation. Meanwhile, jobs do not require a specific order for visiting workstations. This paper considers a static, deterministic version of the problem in which jobs do not have to visit all workstations, the workstations do not necessarily have identical machines, and the processing times depend on both the job and the machine. The objective is to minimize the maximum completion time (makespan) which is commensurate with maximizing the utilization of the available machines. To the best of our knowledge, this problem structure has not been considered in the literature before despite its existence in real-life applications. Since it is NP-hard problem, efficient heuristics are needed to generate near optimal solutions in practically acceptable computational times. In this paper, two neighborhood search functions and two solution combination functions are developed and used within a scatter search with path relinking metaheuristic, along with a new distance definition between solutions. Computational experiments are conducted first to select the best levels of the metaheuristic parameters. Then, computational experiments are conducted on specially designed instances that take into consideration different settings of the studied problem. This is followed by computational experiments on a set of benchmark instances of the proportionate multiprocessor open shop scheduling problem which is a special rase of the studied problem for which other metaheuristics have been developed in the literature. Results show that the developed metaheuristic is capable of generating optimal or near-optimal solutions for different configurations of the studied problem. In addition, it generates competitive results for the proportionate raw compared to the available metaheuristics with 18 new upper bounds; among them seven are optimal.

The total maintenance cost can be reduced by grouping maintenance actions of several components.This paper contributes to the existing literature by introducing an enhanced maintenance optimisation approach that considers the effect of maintenance crew loading due to grouping on the maintenance decisions of multi-component systems.A modified mathematical model is firstly developed for evaluating the failure probability function of each component, the remaining useful life and the maintenance cost.Economic and structural dependencies are taken into consideration.A simulation is secondly implemented to provide estimates of the associated costs with changes in the decision variables.Using the simulation model, an optimisation approach based on a genetic algorithm is thirdly developed to minimise the long-term mean maintenance cost per unit time.Computational results show that the proposed maintenance optimisation approach provides considerable maintenance cost savings and emphasises the importance of considering the effect of maintenance crew constraints in maintenance scheduling.

An important element in the integration of the fourth industrial revolution is the development of efficient algorithms to deal with dynamic scheduling problems. In dynamic scheduling, jobs can be admitted during the execution of a given schedule, which necessitates appropriately planned rescheduling decisions for maintaining a high level of performance. In this paper, a dynamic case of the multiprocessor open shop scheduling problem is addressed. This problem appears in different contexts, particularly those involving diagnostic operations in maintenance and health care industries. Two objectives are considered simultaneously—the minimization of the makespan and the minimization of the mean weighted flow time. The former objective aims to sustain efficient utilization of the available resources, while the latter objective helps in maintaining a high customer satisfaction level. An exact algorithm is presented for generating optimal Pareto front solutions. Despite the fact that the studied problem is NP-hard for both objectives, the presented algorithm can be used to solve small instances. This is demonstrated through computational experiments on a testbed of 30 randomly generated instances. The presented algorithm can also be used to generate approximate Pareto front solutions in case computational time needed to find proven optimal solutions for generated sub-problems is found to be excessive. Furthermore, computational results are used to investigate the characteristics of the optimal Pareto front of the studied problem. Accordingly, some insights for future metaheuristic developments are drawn.

The declining demand for fluorescent lamps, along with a recent currency floatation, forced a major glass tube manufacturer in Egypt to adopt a mixed Make-to-Stock (MTS) - Make-to-Order (MTO) strategy. In this research, a production control policy is proposed to effectively guide the involved product-mix decisions towards reducing the total cost. A simulation model is developed which is divided into three interconnected modules, namely decision, production, and order fulfillment. Along with the simulation model, a randomized search algorithm is applied to find appropriate values for the control variables of the proposed policy. Results provide evidence for the effectiveness of the proposed production control policy in reducing the total cost. Sensitivity analyses are conducted to investigate the effects of raw material and energy prices on the production parameters and the control variables of the proposed policy. It is found that the increase in raw material prices influences the production parameters; however, it does not affect the control variables of the proposed policy. On the other hand, the increase in energy prices influences both.

This paper considers a decision making problem encountered by a natural gas pipeline construction company having a set of ongoing projects and facing unpredictable risks that can result in large deviations from planned schedules. This situation forces the company to consider the decision of halting one or more projects to avoid future losses and to allow for possible reallocation of some of their resources to other ongoing projects. This decision making problem involves different factors and criteria that need to be combined in an organized structure that exploits assessments of experts managing such projects. The analytic hierarchy process (AHP) is found to be suitable for guiding decisions in this problem. A case study for a major natural gas pipeline construction company in Egypt is presented, where three ongoing projects are considered. The proposed AHP structure, along with collected pairwise comparison scores and calculated priorities, suggests halting one project. Sensitivity analysis is conducted to investigate the effect of changes in the pairwise comparison scores assigned to the main criteria on the final decision. The results and analysis provide some insights regarding the application of the AHP and the relative importance of the factors affecting decisions.

The assignment of stores in shopping centers is a challenging task due to conflicting factors related to the accessibility of store locations and the power of attraction of the competing brands. In a previous work, the Authors proposed an evenhanded approach of assigning stores to empty locations in shopping centers, aiming to balance the distribution of flow across all shopping center areas (blocks). A mixed integer linear programming (MILP) model was devised targeting the minimization of the differences of flows between blocks. Because of the complexity and the relatively large size of the problem in real life, a solution algorithm based on tabu search (TS) is proposed in this sequel paper to provide efficient solutions. TS features such as tabu list, tabu tenure, aspiration criteria, short and long-term memory, and diversification are developed to improve the search process. The proposed TS algorithm is tested on a number of generated instances in a numerical study. Results prove the efficiency of the algorithm in solving large size instances for which exact methods cannot obtain feasible solutions in reasonable time.

Finding a minimum spanning tree in a given network is a famous combinatorial optimization problem that appears in different engineering applications. Even though this problem is solvable in polynomial time, having efficient mathematical programming models is important as they can provide insights for formulating larger models that integrate other decisions in more complex applications. In the literature, there are ten different integer and mixed integer linear programming (MILP) models for this problem. They are variants of set packing, cuts, network flow and node level formulations. In addition, this paper introduces an efficient node level MILP model. Comparisons for the eleven models are provided. First, the models are compared in terms of the number of decision variables and the number of constraints. Then, computational comparisons using a commercial MILP solver on sets of randomly generated instances of different sizes are conducted. Results provide evidence that the proposed MILP model is competitive in terms of the computational time needed for proving optimality of generated solutions for instances with up to 50 nodes. Meanwhile, the LP relaxation of a multi-commodity flow MILP model which has integer polyhedron provides stable computational times despite its larger size.

A mixed integer linear programming (MILP) model for production planning in garment industry is developed. The model considers capacity and financial planning decisions for mixed make-to-order (MTO)/make-to-stock (MTS) environment when demand exhibits predictable fluctuations. In the literature, existing models present little focus for capacity distribution between MTO and MTS products along with the effect of the cash availability on the production decisions. The developed model is applied to a real-life case study in Egypt, and the sensitivity of the results are analyzed. The model was very sensitive to the increases in the fabric prices and subcontracting costs while the overall net profits were not significantly affected by the changes in the inventory holding costs. The amount of MTS production increases with cash availability; while partitioning the capacity to 60% and 40% for MTO and MTS products respectively proved to be the best option and found to have a significant contribution on the revenues and in maintaining financial stability.

This work is concerned with the process design of multistage deep drawing, where an integrated artificial intelligence (AI) approach is presented with a special focus on box-shaped parts. This approach combines three AI tools, namely part shape recognition, expert system for process design governing rules, and search and optimization via dynamic programming. Validation and final selection of optimized process plans are done using finite element analysis with full account of the formability limits of the material used. The main advantage of the proposed integrated approach is its capability of generating valid, optimized process plans in a relatively short time compared to traditional approaches. Two case studies are presented for demonstrating its effectiveness.

Overcrowding in emergency departments (EDs) is a serious problem that can harm patients and lead to negative operational and financial performances for hospitals. This paper integrates the Six Sigma methodology with discrete event simulation (DES) to guide improvement decisions, in which we target the reduction of overcrowding in EDs with a special attention on the medical equipment utilization and the influence of changing the medical equipment technology on patients’ waiting time and consequently their satisfaction. The Six Sigma methodology, based on the “Define, Measure, Analyze, Improve, and Control (DMAIC)” format, is used to analyze the EDs overcrowding problem, diagnose its causes, and control the performance improvement plans. The DES is used within the “Improve” phase in order to provide a prognosis for the expected performance under the proposed improvement scenarios and to evaluate their effects on the ED performance measures. This research investigates the benefits of the application of the quality improvement methods in the Egyptian healthcare system which has different characteristics compared to developed countries. A case study in a private tertiary hospital is used in the current investigation. We propose process-based modifications that can help reduce the overcrowding problem, increase patient throughput, and reduce patient length of stay. The case study demonstrates the effectiveness of using the integrated Six Sigma-DES approach on reducing the ED crowdedness. The use of DES in the “Improve” phase provides inexpensive assessments of the improvement alternatives and eliminates the troubles associated with real system modifications.